Video monitoring is an important part of a safeguard system, in which one key issue is how to calibrate surveillance cameras in a monitoring scene to obtain their intrinsic parameters (such as a coordinate of a principle point, a focal length and a distortion factor) and external parameters (such as a rotation matrix and a translation vector of a coordinate system of the surveillance camera with respect to a universal coordinate system) thereof, so as to obtain a relative erecting position, a facing direction and a viewing filed of each surveillance camera in the safeguard system.
Currently, there are three calibrating methods for the surveillance camera: a conventional calibrating method, a calibrating method based on active vision and a self-calibrating method. In the conventional calibrating method, an precisely machined calibration object (such a calibration block, a calibration plate or a calibration bar) is placed in the viewing field of the surveillance camera, and then the intrinsic and external parameters of the surveillance camera are obtained by establishing a relationship between a given coordinate of a point on the calibration object and a coordinate of the point on an image. This method wastes time and energy because above operation is required to be performed on each surveillance camera, and only the intrinsic parameters can be calibrated by this method. Furthermore, since the outdoor monitoring scene is usually large and a size of the calibration object is relatively small and occupies a little proportion of the image, a calibration error of this method is large. In the calibrating method based on active vision, the surveillance camera is controlled actively to move in a special mode (such as a pure translation motion or a rotation around an optical center), and the intrinsic parameters of the surveillance camera are calculated according to the specificity of the motion. However, since most of the surveillance cameras are installed on the fixed location and the motion thereof is hard to control, this method has a poor applicability. Furthermore, with this method, it is also required to calibrate each surveillance camera respectively, thus wasting time and energy, and moreover, the external parameters cannot be calibrated. In the self-calibrating method, no calibration object is needed and the surveillance camera is not required to move in the special mode. The intrinsic and external parameters of the surveillance camera is directly calibrated according to the relationship between pixels on a plurality of images sampled by the surveillance camera (maybe a plurality of surveillance cameras) and constraints of the intrinsic and external parameters. Some self-calibrating methods can calibrate a plurality of surveillance cameras simultaneously by means of a multiple view geometry method. However, the viewing fields of the plurality of surveillance cameras are required to have a large area overlapped with each other, otherwise the plurality of surveillance cameras will not be calibrated due to an image matching failure. However, in practice, the overlapped area between viewing fields of the plurality of surveillance cameras is small. Thus, the currently known self-calibrating methods have difficulties in calibrating the plurality of surveillance cameras simultaneously.